jevjam

Typed decisions (choice, score, yes/no) from Laya, Julia-1 and clef-flash on your own GPU

OtherPythonv0.3.3

jevjam

Self-hosted MCP server and Jev-compatible HTTP API for small decision models, on one GPU, in Docker.

Ask a model typed questions about a piece of text, JSON, an image or a video, and get calibrated answers back in milliseconds: pick a label (choice), rate on a scale (score), or answer yes or no (noul). Agents call it as MCP tools; services call POST /v1/systemone, the same protocol as TypeSafe Jev, so a Jev client only needs a new base URL.

Use it to route tickets, flag abuse, guard tool calls, pick a model for a prompt, or any other decision you would rather not spend a large LLM call on.

Models

ModelBySizeReadsPicked when
LayaConvai Innovations3 checkpoints, ~1.2B in alltext, JSONby default; its router picks English, multilingual or typed-decisions
Julia-1Supersonic Labs144Mtext, JSONthe request names julia-1
clef-flashCloudflare9Btext, JSON, images, videothe request names clef-flash

One checkpoint stays in VRAM at a time, and it is freed after five idle minutes. See docs/models.md for sizes, quantization and limits.

Quick start

You need Docker, an NVIDIA GPU, and the NVIDIA Container Toolkit.

docker run -d --name jevjam --gpus all -p 127.0.0.1:8000:8000 \
  -v jevjam-models:/models ghcr.io/beremaran/jevjam:latest

Or, from a clone, docker compose up -d. No model downloads at boot; the first request fetches what it needs into the jevjam-models volume, which takes minutes once.

Ask over HTTP:

curl -s http://127.0.0.1:8000/v1/systemone -H 'Content-Type: application/json' -d '{
  "state": "We were billed twice for March. Refund it today or we cancel.",
  "questions": {
    "department": {"type": "choice", "instructions": "Who should handle this?",
                   "criteria": {"billing": "payments, refunds", "technical": "bugs, outages"}},
    "churn_risk": {"type": "noul", "instructions": "Does the user threaten to leave?"}
  }
}'

The answer, trimmed:

{
  "answers": {
    "department": {"type": "choice", "choice": "billing", "probabilities": {"billing": 0.97, "technical": 0.03}, ...},
    "churn_risk": {"type": "noul", "noul": 0.82, ...}
  },
  "routing": {"model": "english", "reason": "English Latin text", ...}
}

Or connect an agent over MCP, at http://127.0.0.1:8000/mcp:

claude mcp add --transport http jevjam http://127.0.0.1:8000/mcp   # Claude Code
codex mcp add jevjam --url http://127.0.0.1:8000/mcp                 # Codex

Agents get four tools: jevjam_predict, jevjam_preset (guard, moderation, triage, model_router), jevjam_route and jevjam_status. The MCP guide covers OpenCode, Pi, remote access and reverse proxies.

Features

  • One process, two doors. The HTTP API and MCP share one queue and one resident model, so neither starves the other of VRAM.
  • Sleeps when idle. After JEVJAM_IDLE_TIMEOUT seconds (300 by default) every checkpoint is freed and the GPU memory goes back to the driver. The next request loads only what it needs; Laya wakes in 0.6 s on an RTX 4070 Ti SUPER.
  • Fits the card it finds. clef-flash loads in BF16, 8-bit, 4-bit, or split across GPU and CPU, whichever fits.
  • Drop-in for Jev. Same request and response shapes; unknown fields are ignored.
  • Locked down by default. Runs as non-root, binds to loopback in Compose, and takes an optional bearer key (JEVJAM_API_KEY) for both endpoints.

Docs

GuideWhat is in it
ConfigurationRunning, settings, the model cache, sleeping on idle
MCP serverTools, auth, client setup, reverse proxies
HTTP API/health, /v1/systemone, question types, JSON Schema, errors
ModelsLaya, Julia-1 and clef-flash: sizes, VRAM, limits

Moving from laya-docker

This repo used to be laya-docker. The old image, ghcr.io/beremaran/laya-docker, gets no more updates; switch to ghcr.io/beremaran/jevjam. Old LAYA_* settings still work and log a warning; see Configuration.

Contributing

Bug reports and pull requests are welcome; see CONTRIBUTING.md. Report security problems privately, as SECURITY.md describes.

License

jevjam is licensed under Apache-2.0. The image also contains the Apache-2.0 Laya package and checkpoints by Convai Innovations, the Apache-2.0 Julia-1 code and checkpoint by Supersonic Labs, and the Apache-2.0 clef-flash code and checkpoint by Cloudflare. The clef-flash code is copied into src/jevjam/vendor/ with its license.

Installation

Source-derived launch command. Check the maintainer’s required arguments and credentials before running:

bash
docker run -i --rm ghcr.io/beremaran/jevjam:0.3.3

Set up in your AI client

Merge this template into ~/Library/Application Support/Claude/claude_desktop_config.json. Keep existing servers. Add any arguments, credentials, and permissions required by the maintainer; this template has not been install-tested.

json
{
  "mcpServers": {
    "io-github-beremaran-jevjam": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/beremaran/jevjam:0.3.3"
      ]
    }
  }
}

Restart Claude Desktop completely for changes to take effect. Confirm the server appears connected in the client’s tool list, then try a read-only example from its documentation.

Claude Desktop setup reference

Package

ghcr.io/beremaran/jevjam:0.3.3docker

Compatible MCP Clients

jevjam works with any MCP-compatible client. Copy the config snippet from the Configuration section above and add it to the file shown for your client, then restart the application.

  • Claude Desktop~/Library/Application Support/Claude/claude_desktop_config.jsonRestart Claude Desktop completely for changes to take effect.
  • Cursor~/.cursor/mcp.jsonRestart Cursor for changes to take effect.
  • VS Code.vscode/mcp.jsonReload VS Code window for changes to take effect.
  • Windsurf~/.codeium/windsurf/mcp_config.jsonRestart Windsurf for changes to take effect.
  • Claude Code.mcp.jsonSave at the project root, then start Claude Code in that project and review the MCP server approval prompt. Keep real credentials out of shared files.

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